Overview
Staff Data Scientist for Finance, leading Snowflake’s driver-based revenue modeling program to build reliable, explainable, production-grade decision systems.
What you'll do
- Own and scale a standardized driver-based revenue modeling framework across product categories.
- Define driver trees, attribution rules, measurement standards, assumptions, and taxonomies.
- Develop statistical, econometric, and machine learning methods to identify leading indicators and causal relationships.
- Forecast key drivers and revenue across short- and long-range horizons using multiple modeling approaches.
- Build self-service scenario, decomposition, and what-if tools with monthly and multi-year views.
- Set standards for evaluation, backtesting, stability testing, reconciliation, confidence intervals, and change documentation.
- Productionize and operate frequently refreshed pipelines and applications with quality gates and monitoring.
What you'll need
- 5+ years building and operating production-grade statistical, forecasting, econometric, or ML systems with business impact.
- Advanced degree in a quantitative field (or equivalent practical experience).
- Strong hands-on experience with business-critical forecasting or driver/unit-economics modeling.
- Deep modeling skills including time-series forecasting and causal inference, plus panel/cohort and probabilistic methods.
- Ability to work with imperfect telemetry and define defensible assumptions while identifying data gaps.
- Strong proficiency in Python and SQL for large datasets and productionization.
- Experience with large-scale data systems and modern data platforms such as Snowflake, BigQuery, Redshift, or Spark.
Details
- Role involves partnering with Product Finance, Product Data Science, Finance Data and Analytics, Analytics Engineering, Product, and go-to-market teams.
Read the full description and apply on the company’s own careers page.
